This contribution presents a new robust decomposition methodology for generating optimal flexible process flow sheets with a large number of uncertain parameters. During the initial steps, first-stage variables are determined by performing mixed-integer nonlinear programming (MINLP) synthesis of a flow sheet at the nominal conditions, and then by exposing the obtained flow sheet sequentially over a set of extreme MINLP scenarios of uncertain parameters. As a result, the sizes of the flow-sheet

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... nits gradually increase, and/or new units are added until the required feasibility is achieved. After testing the flexibility of the obtained design, a Monte Carlo stochastic optimization of the second-stage variables is performed using a sampling method in order to obtain an optimum value of the expected objective variable. The advantages of the proposed methodology are the independence of process model sizes from the number of uncertain parameters, the straightforward use of deterministic models for incorporating uncertainty, and relatively simple execution of MINLP synthesis of processes under uncertainty. Thus, it could be used for designing large processes with a large number of uncertain parameters. The methodology is illustrated by synthesis of a flexible Heat Exchanger Network.